An Empirical Study on Database Usage in Microservices

TL;DR

This empirical study analyzes how microservices architectures utilize various database technologies over 15 years, revealing prevalent patterns, combinations, and trends in database usage across numerous GitHub projects.

Contribution

It provides the first comprehensive empirical analysis of database usage in microservices, including a dataset and insights into technology adoption and combinations.

Findings

Microservices mainly use Relational, Key-Value, Document, and Search databases.

52% of microservices combine multiple database categories.

Older systems favor Relational databases, while newer ones adopt Key-Value and Document technologies.

Abstract

Microservices architectures are an integral part of modern software development. Their adoption brings significant changes to database management. Instead of relying on a single database, a microservices architecture is typically composed of multiple, smaller, heterogeneous, and distributed DBs. In these data-intensive systems, the variety and combination of database categories and technologies play a crucial role in storing and managing data. While data management in microservices is a major challenge, research literature is scarce. We present an empirical study on how databases are used in microservices. On the dataset we collected (and released as open data for future research), considering 15 years of microservices, we examine ca. 1,000 GitHub projects that use databases selected among 180 technologies from 14 categories. We perform a comprehensive analysis of current practices, providing researchers and practitioners with empirical evidence to better understand database usage in microservices. We report 18 findings and 9 recommendations. We show that microservices predominantly use Relational, Key-Value, Document, and Search databases. Notably, 52% of microservices combine multiple database categories. Complexity correlates with database count, with older systems favoring Relational databases and newer ones increasingly adopting Key-Value and Document technologies. Niche databases (e.g., EventStoreDB, PostGIS), while not widespread, are often combined with a mainstream one.